Falls can be one of the most damaging events that elders may experience in their lives, especially when they live alone. The impact of a fall can vary from minor bruises, to life altering fractures and even become fatal. The purpose of this study is to establish a novel non-contact radar method of detecting an elderly fall when occurred in home staying. The novelty of the proposed detection technique is the exploitation of a 1D effective acceleration derived from Short Time Fourier Transform (STFT). This technique was tested utilizing a 2.45 GHz Continuous Wave (CW) Radar implemented with a Software Defined Radio (SDR) and low-cost, off-the-shelf components. Herein, we present test results that classify incidents as either falls or non-falls in line-of-sight cases. Firstly, the results are compared with the corresponding values measured with a commercial marker-based optoelectronic motion capture multi-camera system (VICON) showing high similarity. Furthermore, real-time scenarios were conducted to estimate the accuracy and the number of false alarms of the proposed method. The proposed algorithm is proved capable of exploiting the Power Burst Curve (PBC) as a preliminary factor to yield an efficient fall incident classifier based on the effective acceleration, while minimizing the required processing resources.

A novel non-contact method is presented to detect the fall of elderly people in indoor environment. A Continuous Wave (CW) Radar was used to calculate the spectrum of the moving/falling elder using Short Time Fourier Transform (STFT). The Power Burst Curve (PBC) and the acceleration were derived from the STFT and used as features for the classifier. This technique was tested with a 2.45 GHz CW Radar implemented by a Software Defined Radio (Ettus Research USRP N210) and off the shelf low cost components. Especially, the frequency of operation was used because it belongs to free Industrial Scientific and Medical (ISM) band. Furthermore, the frequency was chosen in order to be able to penetrate walls. The antenna selected to be as compact as possible (printed antenna) in order to be placed inside a room avoiding any disturbance. Finally, we present test results classifying between falls or non-falls for both line of sight and behind the wall cases. The comparisons show a good performance in real time applications.

IntroductionRecent advances in Artificial Intelligence (AI) and Computer Vision (CV) have led to automated pose estimation algorithms using simple 2D videos. This has created the potential to perform kinematic measurements without the need for specialized, and often expensive, equipment. Even though there's a growing body of literature on the development and validation of such algorithms for practical use, they haven't been adopted by health professionals. As a result, manual video annotation tools remain pretty common. Part of the reason is that the pose estimation modules can be erratic, producing errors that are difficult to rectify. Because of that, health professionals prefer the use of tried and true methods despite the time and cost savings pose estimation can offer.MethodsIn this work, the gait cycle of a sample of the elderly population on a split-belt treadmill is examined. The Openpose (OP) and Mediapipe (MP) AI pose estimation algorithms are compared to joint kinematics from a marker-based 3D motion capture system (Vicon), as well as from a video annotation tool designed for biomechanics (Kinovea). Bland-Altman (B-A) graphs and Statistical Parametric Mapping (SPM) are used to identify regions of statistically significant difference.ResultsResults showed that pose estimation can achieve motion tracking comparable to marker-based systems but struggle to identify joints that exhibit small, but crucial motion.DiscussionJoints such as the ankle, can suffer from misidentification of their anatomical landmarks. Manual tools don't have that problem, but the user will introduce a static offset across the measurements. It is proposed that an AI-powered video annotation tool that allows the user to correct errors would bring the benefits of pose estimation to professionals at a low cost.

As the aged population is rapidly increased, the need for efficient and low-cost ambient systems becomes vital. The effectiveness of such systems lies upon the accurate and fast motion analysis in order to predict the elderly's action and develop systems to act in need. To achieve that, the precise estimation of the entire human body pose is often exploited, providing the required motion-related information. Yet, the exploitation of the entire human pose can present several limitations. The paper at hand exploits state-of-the-art data-driven classifiers and compares their efficiency in action recognition based on a specific set of joints or coordinates, i.e., the x, y and z-axis. The above rests upon the notion that each action in real life can be effectively perceived by observing only a specific set of joints. Considering that, we aim to investigate the capacity of such a joint analysis and its ability to deliver an enhanced pose-based action recognition system. To that end, we correlate specific joints with each action, indicating the joints that contribute the most. We evaluate our findings on two different senior subjects using two different classifiers, viz., support vector machine (SVM) and convolutional neural network (CNN), showing that the above strategy can improve recognition rates.